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1. Measuring Mental Demand Using Pupil Dilation in an Augmented Reality Environment

   https://doi.org/10.1177/10711813251358793  

   Mohanty, Siddarth; Kim, Jung Hyup; Mostowfi, Sara; Wang, Fang; Oprean, Danielle; Seo, Kangwon; Wang, Yi (July 2025, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   This study investigates the method for measuring cognitive workload in augmented reality-based biomechanics lectures by analyzing pupil dilation. Using Dikablis Glasses 3 and Microsoft HoloLens, we recorded physiological and subjective data across learning and problem-solving phases. Pupil dilation was normalized and segmented, enabling a comparison of cognitive demands between phases. The results indicated significant correlations between pupil dilation and NASA TLX cognitive demand, particularly in lectures that primarily involved procedural knowledge. These findings suggest that instructional design and content complexity have a significant impact on cognitive load, providing valuable insights for optimizing AR-based learning environments to support cognitive efficiency and student engagement. 

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2. Measuring Cognitive Workload in Augmented Reality Learning Environments Through Pupil Area Analysis

   https://doi.org/10.1007/978-3-031-61569-6_11  

   Mohanty, Siddarth; Kim, Jung Hyup; Pulipati, Varun; Wang, Fang; Mostowfi, Sara; Oprean, Danielle; Wang, Yi; Seo, Kangwon (January 2024, Springer Nature Switzerland)

   In the digital learning landscape, Augmented Reality (AR) is revolutionizing instructional methodologies. This study shifts focus to explore the impact of AR-based lectures on pupil dilation as a biomarker of mental demand. By analyzing pupil dilation with cognitive load assessment tools like the NASA Task Load Index, we aim to understand the cognitive implications of prolonged exposure to AR in educational settings. We hypothesize that variations in pupil size can be indicative of cognitive load, correlating with the mental demands imposed by AR lectures. Preliminary findings suggest a significant relationship between increased pupil dilation and heightened mental workload during AR engagements. This study highlights a new way to measure cognitive workload in AR environments using pupil dilation data. 

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3. Pupil Dilation as an Indicator of Debugging Strategy in a Location-Based AR Learning Environment

   https://doi.org/10.1177/10711813251358807  

   Mostowfi, Sara; Kim, Jung Hyup; Mohanty, Siddarth; Wang, Fang; Oprean, Danielle; Wang, Yi; Seo, Kangwon (October 2025, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Debugging process plays a crucial role in helping students pinpoint their specific learning weaknesses, allowing them to modify their strategies for enhanced academic performance. Notably, changes in pupil dilation serve as an indicator of arousal associated with confronting learning challenges. This physiological response acts as a “physiological footprint” that reflects cognitive engagement, facilitating internally focused cognitive processes such as insight generation and mind-wandering. In this study, we proposed that pupil dilation could be a valuable predictor of students’ metacognitive awareness throughout the debugging process, specifically within an augmented reality (AR) learning environment. The findings revealed significant differences in pupil dilation among students categorized by their varying levels of debugging, which represents a specific dimension of the Metacognitive Awareness Inventory. 

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4. The Findings and Developed Tools from the Exploration of Procedural Pathways to Bolster Algebraic Knowledge

   Pradhan, Siddhartha (April 2024, WPI eprojects)

   Algebra is essential for delving into advanced mathematical topics and STEM courses (Chen, 2013), requiring students to apply various problem-solving strategies to solve algebraic problems (Common Core, 2010). Yet, many students struggle with learning basic algebraic concepts (National Mathematics Advisory Panel (NMAP), 2008). Over the years, both researchers and developers have created a diverse set of educational technology tools and systems to support algebraic learning, especially in facilitating the acquisition of problem- solving strategies and procedural pathways. However, there are very few studies that examine the variable strategies, decisions, and procedural pathways during mathematical problem- solving that may provide further insight into a student’s algebraic knowledge and thinking. Such research has the potential to bolster algebraic knowledge and create a more adaptive and personalized learning environment. This multi-study project explores the effects of various problem-solving strategies on students’ future mathematics performance within the gamified algebraic learning platform From Here to There! (FH2T). Together, these four studies focus on classifying, visualizing, and predicting the procedural pathways students adopted, transitioning from a start state to a goal state in solving algebraic problems. By dissecting the nature of these pathways—optimal, sub-optimal, incomplete, and dead-end—we sought to develop tools and algorithms that could infer strategic thinking that correlated with post-test outcomes. A striking observation across studies was that students who frequently engaged in what we term ‘regular dead-ending behavior’, were more likely to demonstrate higher post-test performance, and conceptual and procedural knowledge. This finding underscores the potential of exploratory learner behavior within a low-stakes gamified framework in bolstering algebraic comprehension. The implications of these findings are twofold: they accentuate the significance of tailoring gamified platforms to student behaviors and highlight the potential benefits of fostering an environment that promotes exploration without retribution. Moreover, these insights hint at the notion that fostering exploratory behavior could be instrumental in cultivating mathematical flexibility. Additionally, the developed tools and findings from the studies, paired with other commonly used student performance metrics and visualizations are used to create a collaborative dashboard–with teachers, for teachers. 

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5. Using Learning Maps and Bloom’s Taxonomy to Develop a New Instrument to Assess Knowledge Transfer from Physics to Statics Courses.

   https://doi.org/10.18260/1-2--57307  

   Wijesinghe, Priyantha; Seneviratne, Varuni; Medsker, Larry; Giles, Courtney; Ottati, Marlee (June 2025, ASEE Conferences)

   This paper presents the design and analysis of a pilot problem set deployed to engineering students to assess their retention of physics knowledge at the start of a statics course. The problem set was developed using the NSF-IUSE (grant #2315492) Learning Map project (LMap) and piloted in the spring and fall of 2024. The LMap process is rooted in the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model [1] and Backward Design [2,3], extending these principles to course sequences to align learning outcomes, assessments, and instructional practices. The primary motivation for this problem set (Statics Knowledge Inventory, SKI) was to evaluate students' understanding and retention of physics concepts at the beginning of a statics course. The SKI includes a combination of multiple-choice questions (MCQ) and procedural problems, filling a gap in widely-used concept inventories for physics and statics, such as the Force Concept Inventory (FCI) and Statics Concept Inventory (SCI), which evaluate learning gains within a course, rather than knowledge retention across courses. Using the LMap analysis and instructor consultations, we identified overlapping concepts and topics between Physics and Statics courses, referred to here as “interdependent learning outcomes” (ILOs). The problem set includes 15 questions—eight MCQs and seven procedural problems. Unlike most concept inventories, procedural problems were added to provide insight into students’ problem-solving approach and conceptual understanding. These problems require students to perform calculations, demonstrate their work, and assess their conceptual understanding of key topics, and allow the instructors to assess essential prerequisite skills like drawing free-body diagrams (FBDs), computing forces and moments, and performing basic vector calculation and unit conversions. Problems were selected and adapted from physics and statics textbooks, supplemented by instructor-designed questions to ensure full coverage of the ILOs. We used the revised 2D Bloom’s Taxonomy [4] and a 3D representation of it [5] to classify each problem within a 6x4 matrix (six cognitive processes x four knowledge dimensions). This classification provided instructors and students with a clear understanding of the cognitive level required for each problem. Additionally, we measured students’ perceived confidence and difficulty in each problem using two questions on a 3-point Likert scale. The first iteration of the problem set was administered to 19 students in the spring 2024 statics course. After analyzing their performance, we identified areas for improvement and revised the problem set, removing repetitive MCQs and restructuring the procedural problems into scaffolded, multi-part questions with associated rubrics for evaluation. The revised version, consisting of five MCQs and six procedural problems, was deployed to 136 students in the fall 2024 statics course. A randomly selected subset of student answers from the second iteration was graded and analyzed to compare with the first. This analysis will inform future efforts to evaluate knowledge retention and transfer in key skills across sequential courses. In collaboration with research teams developing concept inventories for mechanics courses, we aim to integrate these procedural problems into future inventories. 

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